FSD Beta V11.4 Rolls Out to Tesla Employees

Image: Tesla

Tesla FSD Beta v11.4 begins rolling out to company employees today, Monday. As confidence grows, the updated version will be rolled out to the company's customers, Elon Musk said.

In response to a question about the start date for the rollout of FSD Beta v11.4, Tesla CEO Musk said on Sunday that it will be delivered to employees on Monday. He specified that as confidence in it grows, wider deployment will follow. The company's customers have been expecting the new version for several weeks now since v11.4 was rolled out to a limited number of the company's employees at the end of April. Musk elaborated that Tesla “test as much as possible in simulation and with QA drivers, but reality is vastly more complex.”

Ships to Tesla employees tomorrow, then progressively wider as confidence grows.

We test as much as possible in simulation and with QA drivers, but reality is vastly more complex.

— Elon Musk (@elonmusk) May 7, 2023

Musk personally tested the update last week. He said it was excellent. “V11.4 is excellent. Several days of driving to random pin drop locations in Austin. Zero safety-critical interventions,” he said.

In another tweet on Sunday, Musk added that there are “major improvements” in the new update. He added that V11.4 should arguably be V12.0. However, V12 is reserved for when FSD is end-to-end AI, according to him.

Full release notes of V11.4:

• Improved the decision to assert or yield for pedestrians at more crosswalks by evaluating multiple possible futures in the joint space of ego's actions and the pedestrian's response.
• Improved ego‘s behavior near VRUs by measuring their probability of intersecting ego's path, based on their kinematic data, and preemptively decelerating when the estimated risk is high.
• Improved turn performance in dense unstructured city environments. Examples of improved cases include: turning when the turn lane is blocked by parked cars and avoiding turning into bus lanes.
• Improved lane guidance module to feed in long range routing "hints" to the network for which lanes ego needs to be in to reach its destination. Also significantly improved per—lane routing type autolabeler. These changes combined resolved 64% of all interventions caused by bad routing type.
• Improved geometric consistency between lane, line, road edge and restricted space detections by re-training our networks on the same dataset with the latest version of our "lane guidance" module, and by using a common features space to predict line, road edge and restricted space.
• Improved recall for partial cut-ins by 39% and precision for false positive cut-ins due to lane changes into adjacent lanes by 66%, resulting in a 33% reduction in overall lane-changing prediction error. This was accomplished by further increasing our auto-labeled fleet dataset by 80k clips, improving the accuracy of the auto-labeling algorithm, and tuning the distribution of training supervision.
• Improved understanding for when to use bus lanes and when to avoid them, by updating the lane type detection network and improving map-vision fusion.
• Improved speed control during lane changes through better consideration of upcoming navigation deadlines, required back-to-back lane changes and presence of a vehicle behind ego.
• Added new Vision Speed network to infer the typical driving speed on a given road. This is used to limit the maximum allowed speed in environments such as parking lots and residential roads.
• Mitigated hydroplaning risk by making maximum allowable speed in Autopilot proportional to the severity of the detected road conditions. In extreme cases, Autopilot may use the wetness of the road, tire spray from other vehicles, rain intensity, tire wear estimation or other risk factors that indicate the vehicle is near the handling limit of the surface to warn the driver and reduce speed.
• Improved long-range path blockage detection and control on city streets. Ego will now be able to perform lane changes due to upcoming path blockages earlier.
• Improved developer productivity with better code diagnostics and C++20 features by upgrading compiler to clang-16. This also improved photon-to-control vehicle response latency by 2%.

© 2023, Eva Fox | Tesmanian. All rights reserved.

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